diff --git a/tensorrt_llm/_torch/pyexecutor/sampler.py b/tensorrt_llm/_torch/pyexecutor/sampler.py
index d2ee22a214c..9726b69e864 100644
--- a/tensorrt_llm/_torch/pyexecutor/sampler.py
+++ b/tensorrt_llm/_torch/pyexecutor/sampler.py
@@ -709,6 +709,9 @@ def _process_requests(self,
                 batched_strategy = strategies[0]
             else:
                 batched_strategy = None
+        else:
+            assert len(set(strategies)) == 1, "mixed sampler is not enabled"
+            batched_strategy = strategies[0]
         generator = self.get_generator(raw_logits.device)
         if batched_strategy is not None:
             logits = raw_logits[:sum_steps]
diff --git a/tensorrt_llm/serve/openai_protocol.py b/tensorrt_llm/serve/openai_protocol.py
index 3ba66ab5fc0..abf202f31fb 100644
--- a/tensorrt_llm/serve/openai_protocol.py
+++ b/tensorrt_llm/serve/openai_protocol.py
@@ -220,13 +220,12 @@ class CompletionRequest(OpenAIBaseModel):
     stream_options: Optional[StreamOptions] = None
     suffix: Optional[str] = None
     temperature: Optional[float] = 1.0
-    top_p: Optional[float] = 1.0
+    top_p: Optional[float] = Field(None)
     user: Optional[str] = None
     lora_request: Optional[LoRARequest] = None
 
     # doc: begin-completion-sampling-params
     use_beam_search: bool = False
-    top_k: int = 0
     top_p_min: float = 0.0
     min_p: float = 0.0
     repetition_penalty: float = 1.0
@@ -279,7 +278,6 @@ def to_sampling_params(self, vocab_size: int = 32000) -> SamplingParams:
 
             # completion-sampling-params
             use_beam_search=self.use_beam_search,
-            top_k=self.top_k,
             top_p_min=self.top_p_min if self.top_p_min > 0 else None,
             min_p=self.min_p,
             repetition_penalty=self.repetition_penalty,
@@ -510,7 +508,7 @@ class ChatCompletionRequest(OpenAIBaseModel):
     stream: Optional[bool] = False
     stream_options: Optional[StreamOptions] = None
     temperature: Optional[float] = 1.0
-    top_p: Optional[float] = 1.0
+    top_p: Optional[float] = Field(None)
     tools: Optional[List[ChatCompletionToolsParam]] = None
     tool_choice: Optional[Union[Literal["none", "auto"],
                                 ChatCompletionNamedToolChoiceParam]] = "none"
@@ -527,7 +525,6 @@ class ChatCompletionRequest(OpenAIBaseModel):
     # doc: begin-chat-completion-sampling-params
     best_of: Optional[int] = None
     use_beam_search: bool = False
-    top_k: int = 0
     top_p_min: float = 0.0
     min_p: float = 0.0
     repetition_penalty: float = 1.0
@@ -618,7 +615,6 @@ def to_sampling_params(self,
             # chat-completion-sampling-params
             best_of=self.best_of,
             use_beam_search=self.use_beam_search,
-            top_k=self.top_k,
             top_p=self.top_p,
             top_p_min=self.top_p_min if self.top_p_min > 0 else None,
             min_p=self.min_p,
diff --git a/tests/unittest/_torch/speculative/test_draft_target.py b/tests/unittest/_torch/speculative/test_draft_target.py
index 9aaa81e8375..f21b36bb61c 100644
--- a/tests/unittest/_torch/speculative/test_draft_target.py
+++ b/tests/unittest/_torch/speculative/test_draft_target.py
@@ -14,10 +14,12 @@
 from utils.util import similar
 
 
-@pytest.mark.parametrize("use_cuda_graph,attn_backend",
-                         [[False, "TRTLLM"], [True, "TRTLLM"]])
+@pytest.mark.parametrize(
+    "use_cuda_graph,attn_backend,use_greedy_sampling",
+    [[False, "TRTLLM", True], [True, "TRTLLM", True], [True, "TRTLLM", False]])
 @pytest.mark.high_cuda_memory
-def test_llama_draft_target(use_cuda_graph: bool, attn_backend: str):
+def test_llama_draft_target(use_cuda_graph: bool, attn_backend: str,
+                            use_greedy_sampling: bool):
     total_mem_gb = torch.cuda.get_device_properties(0).total_memory / 1e9
     if total_mem_gb < 60:
         pytest.skip("Not enough memory to load target model")
@@ -52,7 +54,10 @@ def test_llama_draft_target(use_cuda_graph: bool, attn_backend: str):
         "The capital of France is",
         "The president of the United States is",
     ]
-    sampling_params = SamplingParams(max_tokens=32)
+
+    sampling_params = SamplingParams(
+        max_tokens=32) if use_greedy_sampling else SamplingParams(
+            max_tokens=32, top_p=0.9, temperature=1.0)
 
     llm_spec = LLM(**llm_common_config, speculative_config=spec_config)
     results_spec = llm_spec.generate(prompts, sampling_params)
@@ -66,7 +71,9 @@ def test_llama_draft_target(use_cuda_graph: bool, attn_backend: str):
 
     for text_spec, text_ref in zip(generated_text_spec, generated_text_ref):
         # The spec decode algorithm currently guarantees identical results
-        assert similar(text_spec, text_ref)
+        # Skip the reference check for non-greedy sampling as the output is not deterministic
+        if use_greedy_sampling:
+            assert similar(text_spec, text_ref)
 
 
 if __name__ == "__main__":